






'''simple docstring'''







import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
  from scipy.stats import pearsonr, spearmanr
  from sklearn.metrics import fa_score, matthews_corrcoef


__lowerCAmelCase        =							(
    "This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate "
    "library. You can have a look at this example script for pointers: "
    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"
)


def       __UpperCamelCase					(      lowercase_       :  List[Any]   ,       lowercase_       :  List[Any]   ):
       """simple docstring"""




       warnings.warn(lowercase_   ,       lowercase_   )
       requires_backends(lowercase_   ,       'sklearn'   )
       return (preds == labels).mean()


def       __UpperCamelCase					(      lowercase_       :  Optional[Any]   ,       lowercase_       :  Dict   ):
       """simple docstring"""




       warnings.warn(lowercase_   ,       lowercase_   )
       requires_backends(lowercase_   ,       'sklearn'   )
       a_						     =			simple_accuracy(lowercase_   ,       lowercase_   )
       a_						     =			fa_score(y_true=lowercase_   ,       y_pred=lowercase_   )
       return {
           "acc": acc,
           "f1": fa,
           "acc_and_f1": (acc + fa) / 2,
       }


def       __UpperCamelCase					(      lowercase_       :  List[Any]   ,       lowercase_       :  List[Any]   ):
       """simple docstring"""




       warnings.warn(lowercase_   ,       lowercase_   )
       requires_backends(lowercase_   ,       'sklearn'   )
       a_						     =			pearsonr(lowercase_   ,       lowercase_   )[0]
       a_						     =			spearmanr(lowercase_   ,       lowercase_   )[0]
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
           "corr": (pearson_corr + spearman_corr) / 2,
       }


def       __UpperCamelCase					(      lowercase_       :  Optional[Any]   ,       lowercase_       :  List[Any]   ,       lowercase_       :  Optional[int]   ):
       """simple docstring"""




       warnings.warn(lowercase_   ,       lowercase_   )
       requires_backends(lowercase_   ,       'sklearn'   )
       assert len(lowercase_   ) == len(lowercase_   ), F'Predictions and labels have mismatched lengths {len(lowercase_   )} and {len(lowercase_   )}'
       if task_name == "cola":
              return {"mcc": matthews_corrcoef(lowercase_   ,       lowercase_   )}
       elif task_name == "sst-2":
              return {"acc": simple_accuracy(lowercase_   ,       lowercase_   )}
       elif task_name == "mrpc":
              return acc_and_fa(lowercase_   ,       lowercase_   )
       elif task_name == "sts-b":
              return pearson_and_spearman(lowercase_   ,       lowercase_   )
       elif task_name == "qqp":
              return acc_and_fa(lowercase_   ,       lowercase_   )
       elif task_name == "mnli":
              return {"mnli/acc": simple_accuracy(lowercase_   ,       lowercase_   )}
       elif task_name == "mnli-mm":
              return {"mnli-mm/acc": simple_accuracy(lowercase_   ,       lowercase_   )}
       elif task_name == "qnli":
              return {"acc": simple_accuracy(lowercase_   ,       lowercase_   )}
       elif task_name == "rte":
              return {"acc": simple_accuracy(lowercase_   ,       lowercase_   )}
       elif task_name == "wnli":
              return {"acc": simple_accuracy(lowercase_   ,       lowercase_   )}
       elif task_name == "hans":
              return {"acc": simple_accuracy(lowercase_   ,       lowercase_   )}
       else:
              raise KeyError(lowercase_   )


def       __UpperCamelCase					(      lowercase_       :  Tuple   ,       lowercase_       :  Dict   ,       lowercase_       :  str   ):
       """simple docstring"""




       warnings.warn(lowercase_   ,       lowercase_   )
       requires_backends(lowercase_   ,       'sklearn'   )
       if len(lowercase_   ) != len(lowercase_   ):
              raise ValueError(F'Predictions and labels have mismatched lengths {len(lowercase_   )} and {len(lowercase_   )}'   )
       if task_name == "xnli":
              return {"acc": simple_accuracy(lowercase_   ,       lowercase_   )}
       else:
              raise KeyError(lowercase_   )












'''simple docstring'''







import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
__lowerCAmelCase        =							logging.getLogger(__name__)


def       __UpperCamelCase					(      lowercase_       :  str   ):
       """simple docstring"""




       a_						     =			git.Repo(search_parent_directories=lowercase_   )
       a_						     =			{
           'repo_id': str(lowercase_   ),
           'repo_sha': str(repo.head.object.hexsha   ),
           'repo_branch': str(repo.active_branch   ),
       }

       with open(os.path.join(lowercase_   ,       'git_log.json'   )   ,       'w'   ) as f:
              json.dump(lowercase_   ,       lowercase_   ,       indent=4   )


def       __UpperCamelCase					(      lowercase_       :  List[Any]   ):
       """simple docstring"""




       if params.n_gpu <= 0:
              a_						     =			0
              a_						     =			-1
              a_						     =			True
              a_						     =			False
              return

       assert torch.cuda.is_available()

       logger.info('Initializing GPUs'   )
       if params.n_gpu > 1:
              assert params.local_rank != -1

              a_						     =			int(os.environ['WORLD_SIZE']   )
              a_						     =			int(os.environ['N_GPU_NODE']   )
              a_						     =			int(os.environ['RANK']   )

              # number of nodes / node ID
              a_						     =			params.world_size // params.n_gpu_per_node
              a_						     =			params.global_rank // params.n_gpu_per_node
              a_						     =			True

              assert params.n_nodes == int(os.environ['N_NODES']   )
              assert params.node_id == int(os.environ['NODE_RANK']   )

       # local job (single GPU)
       else:
              assert params.local_rank == -1

              a_						     =			1
              a_						     =			0
              a_						     =			0
              a_						     =			0
              a_						     =			1
              a_						     =			1
              a_						     =			False

       # sanity checks
       assert params.n_nodes >= 1
       assert 0 <= params.node_id < params.n_nodes
       assert 0 <= params.local_rank <= params.global_rank < params.world_size
       assert params.world_size == params.n_nodes * params.n_gpu_per_node

       # define whether this is the master process / if we are in multi-node distributed mode
       a_						     =			params.node_id == 0 and params.local_rank == 0
       a_						     =			params.n_nodes > 1

       # summary
       a_						     =			F'--- Global rank: {params.global_rank} - '
       logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes   )
       logger.info(PREFIX + 'Node ID        : %i' % params.node_id   )
       logger.info(PREFIX + 'Local rank     : %i' % params.local_rank   )
       logger.info(PREFIX + 'World size     : %i' % params.world_size   )
       logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node   )
       logger.info(PREFIX + 'Master         : %s' % str(params.is_master   )   )
       logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node   )   )
       logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu   )   )
       logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()   )

       # set GPU device
       torch.cuda.set_device(params.local_rank   )

       # initialize multi-GPU
       if params.multi_gpu:
              logger.info('Initializing PyTorch distributed'   )
              torch.distributed.init_process_group(
                  init_method='env://'   ,       backend='nccl'   ,       )


def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       np.random.seed(args.seed   )
       torch.manual_seed(args.seed   )
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed   )




